Tohoku University · Engineering
Professor Seungkyun Yim's research lab specializes in advanced additive manufacturing, with a focus on powder bed fusion processes such as laser and electron beam melting. The lab investigates fundamental mechanisms governing powder spreading, interfacial bonding, and defect formation under various conditions—including low gravity and complex surface topographies—using a combination of experimental studies, discrete element modeling (DEM), and machine learning. Key research directions include optimizing process parameters, particle size distribution, and spreading strategies to enhance part quality, density, and defect control in multi-material and dissimilar metal components.
Figures are computed from collected data and may differ slightly.
Laser beam powder bed fusion (L-PBF) additive manufacturing offers significant advantages in fabricating multi-material parts with complex geometries and controllable material distributions. In this study, we utilized L-PBF technology to fabricate carbon steel/Al alloy samples with specific bonding strengths by controlling the formation of intermetallic compounds at the liquid/solid interface. Machine learning was employed to optimize the process parameters for the fabrication of carbon steel an
This study was performed to select a preferred seed crystal material for the phosphorus crystallization process through a comparative study of four materials: electron arc furnace, blast furnace and converter slag, and phosphate rock. Leaching and phosphorus removal tests were conducted to evaluate the efficacy of the four materials as seeding agents. Converter slag demonstrated a much larger leaching capacity with respect to calcium and hydroxide ions than did either electron arc furnace or bla
In this study, we proposed alternative spreading techniques aimed at enhancing the powder bed properties in powder bed fusion additive manufacturing, utilizing discrete element simulations. Our findings revealed significant alterations in the powder spreading regime depending on the adopted spreading strategies. In powder spreading with two blades, the climbing regime in the powder pile was eliminated due to the high compressive condition. The powder spreading with two blades can enhance the hom
Understanding powder spreading under low gravity conditions is essential for optimizing final products using additive manufacturing in space. In this study, we investigated the role of gravity on flowability and spreading mechanisms through combined experimental and discrete element method (DEM) studies. Three powders with different theoretical densities were used to reenact low compressive conditions resembling those in a low-gravity environment. The influence of low compressive conditions on f
Controlling internal defects within as-built parts is one of the great interests in the additive manufacturing field. In this study, we explore the powder spreading and defect evolution mechanisms on realistic printing surfaces through a comprehensive multiphysics simulation. The efficacy of a flat surface criterion for internal defect elimination was verified using a machine learning approach. The steady layer thickness in the electron beam melting process was estimated for different printing s
Optimizing powder bed quality is crucial for enhancing the quality of objects manufactured through the powder bed fusion additive manufacturing (PBF-AM) process. In this study, we propose an optimal particle size distribution (PSD) to improve powder bed density and homogeneity during the recoating process. Four PSD types, including unimodal, bimodal, trimodal, and original, were prepared using sieved stainless steel 304 powder for evaluation. The influence of PSDs on flowability and cohesive for
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